COOL-MC

This paper presents COOL-MC, a tool that integrates state-of-the-art reinforcement learning (RL) and model checking.
Specifically, the tool builds upon the OpenAI gym and the probabilistic model checker Storm.
COOL-MC provides the following features: (1) a simulator to train RL policies in the OpenAI gym for MDPs that are defined as input for Storm, (2) a new model builder for Storm, which uses callback functions to verify (neural network) RL policies, (3) formal abstractions that relate models and policies specified in OpenAI gym or Storm, and (4) algorithms to obtain bounds on the performance of so-called permissive policies.
We describe the components and architecture of COOL-MC and demonstrate its features on common benchmarks.

IMPORTANT: We recommend using the docker container from https://github.com/DennisGross/COOL-MC to use the most up-to-date version.